



'''simple docstring'''

import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class 							a__( snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: Union[str, Any]									=      AutoencoderKL
			a_	: Optional[Any]									=      '''sample'''
			a_	: int									=      1e-2






			@property
			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =4
					snake_case__	     =3
					snake_case__	     =(32, 32)

					snake_case__	     =floats_tensor((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)

					return {"sample": image}






			@property
			def 						_lowercase  (     self						)							->							Optional[int]:
					return (3, 32, 32)






			@property
			def 						_lowercase  (     self						)							->							List[str]:
					return (3, 32, 32)






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     ={
					    'block_out_channels': [32, 64],
					    'in_channels': 3,
					    'out_channels': 3,
					    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
					    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
					    'latent_channels': 4,
					}
					snake_case__	     =self.dummy_input
					return init_dict, inputs_dict






			def 						_lowercase  (     self						)							->							Any:
					pass






			def 						_lowercase  (     self						)							->							Optional[Any]:
					pass






			@unittest.skipIf(torch_device == 'mps'					,   'Gradient checkpointing skipped on MPS'						)
			def 						_lowercase  (     self						)							->							Dict:
					# enable deterministic behavior for gradient checkpointing
					snake_case__     ,	snake_case__	     =self.prepare_init_args_and_inputs_for_common()
					snake_case__	     =self.model_class(**_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)

					assert not model.is_gradient_checkpointing and model.training

					snake_case__	     =model(**_UpperCAmelCase						).sample
					# run the backwards pass on the model. For backwards pass, for simplicity purpose,
					# we won't calculate the loss and rather backprop on out.sum()
					model.zero_grad()

					snake_case__	     =torch.randn_like(_UpperCAmelCase						)
					snake_case__	     =(out - labels).mean()
					loss.backward()

					# re-instantiate the model now enabling gradient checkpointing
					snake_case__	     =self.model_class(**_UpperCAmelCase						)
					# clone model
					model_a.load_state_dict(model.state_dict()						)
					model_a.to(_UpperCAmelCase						)
					model_a.enable_gradient_checkpointing()

					assert model_a.is_gradient_checkpointing and model_a.training

					snake_case__	     =model_a(**_UpperCAmelCase						).sample
					# run the backwards pass on the model. For backwards pass, for simplicity purpose,
					# we won't calculate the loss and rather backprop on out.sum()
					model_a.zero_grad()
					snake_case__	     =(out_a - labels).mean()
					loss_a.backward()

					# compare the output and parameters gradients
					self.assertTrue((loss - loss_a).abs() < 1E-5						)
					snake_case__	     =dict(model.named_parameters()						)
					snake_case__	     =dict(model_a.named_parameters()						)
					for name, param in named_params.items():
							self.assertTrue(torch_all_close(param.grad.data					,   named_params_a[name].grad.data					,   atol=5E-5						)						)






			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__     ,	snake_case__	     =AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'					,   output_loading_info=_UpperCAmelCase						)
					self.assertIsNotNone(_UpperCAmelCase						)
					self.assertEqual(len(loading_info['missing_keys']						)					,   0						)

					model.to(_UpperCAmelCase						)
					snake_case__	     =model(**self.dummy_input						)

					assert image is not None, "Make sure output is not None"






			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'						)
					snake_case__	     =model.to(_UpperCAmelCase						)
					model.eval()

					if torch_device == "mps":
							snake_case__	     =torch.manual_seed(0						)
					else:
							snake_case__	     =torch.Generator(device=_UpperCAmelCase						).manual_seed(0						)

					snake_case__	     =torch.randn(
					    1					,   model.config.in_channels					,   model.config.sample_size					,   model.config.sample_size					,   generator=torch.manual_seed(0						)					,   )
					snake_case__	     =image.to(_UpperCAmelCase						)
					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   sample_posterior=_UpperCAmelCase					,   generator=_UpperCAmelCase						).sample

					snake_case__	     =output[0, -1, -3:, -3:].flatten().cpu()

					# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
					# the expected output slices are not the same for CPU and GPU.
					if torch_device == "mps":
							snake_case__	     =torch.tensor(
							    [
							        -4.0_078E-01,
							        -3.8_323E-04,
							        -1.2_681E-01,
							        -1.1_462E-01,
							        2.0_095E-01,
							        1.0_893E-01,
							        -8.8_247E-02,
							        -3.0_361E-01,
							        -9.8_644E-03,
							    ]						)
					elif torch_device == "cpu":
							snake_case__	     =torch.tensor(
							    [-0.1_352, 0.0_878, 0.0_419, -0.0_818, -0.1_069, 0.0_688, -0.1_458, -0.4_446, -0.0_026]						)
					else:
							snake_case__	     =torch.tensor(
							    [-0.2_421, 0.4_642, 0.2_507, -0.0_438, 0.0_682, 0.3_160, -0.2_018, -0.0_727, 0.2_485]						)

					self.assertTrue(torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   rtol=1E-2						)						)





@slow
class 							a__( unittest.TestCase     ):






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							int:
					return f"""gaussian_noise_s={seed}_shape={'_'.join([str(_UpperCAmelCase						) for s in shape]						)}.npy"""






			def 						_lowercase  (     self						)							->							Tuple:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






			def 						_lowercase  (     self					,   _UpperCAmelCase=0					,   _UpperCAmelCase=(4, 3, 512, 512)					,   _UpperCAmelCase=False						)							->							Tuple:
					snake_case__	     =torch.floataa if fpaa else torch.floataa
					snake_case__	     =torch.from_numpy(load_hf_numpy(self.get_file_format(_UpperCAmelCase					,   _UpperCAmelCase						)						)						).to(_UpperCAmelCase						).to(_UpperCAmelCase						)
					return image






			def 						_lowercase  (     self					,   _UpperCAmelCase="CompVis/stable-diffusion-v1-4"					,   _UpperCAmelCase=False						)							->							Union[str, Any]:
					snake_case__	     ='fp16' if fpaa else None
					snake_case__	     =torch.floataa if fpaa else torch.floataa

					snake_case__	     =AutoencoderKL.from_pretrained(
					    _UpperCAmelCase					,   subfolder='vae'					,   torch_dtype=_UpperCAmelCase					,   revision=_UpperCAmelCase					,   )
					model.to(_UpperCAmelCase						).eval()

					return model






			def 						_lowercase  (     self					,   _UpperCAmelCase=0						)							->							int:
					if torch_device == "mps":
							return torch.manual_seed(_UpperCAmelCase						)
					return torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)






			@parameterized.expand(
			    [
			        # fmt: off
			        [33, [-0.1_603, 0.9_878, -0.0_495, -0.0_790, -0.2_709, 0.8_375, -0.2_060, -0.0_824], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
			        [47, [-0.2_376, 0.1_168, 0.1_332, -0.4_840, -0.2_508, -0.0_791, -0.0_493, -0.4_089], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
			        # fmt: on
			    ]						)
			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Tuple:
					snake_case__	     =self.get_sd_vae_model()
					snake_case__	     =self.get_sd_image(_UpperCAmelCase						)
					snake_case__	     =self.get_generator(_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   generator=_UpperCAmelCase					,   sample_posterior=_UpperCAmelCase						).sample

					assert sample.shape == image.shape

					snake_case__	     =sample[-1, -2:, -2:, :2].flatten().float().cpu()
					snake_case__	     =torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice						)

					assert torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   atol=3E-3						)






			@parameterized.expand(
			    [
			        # fmt: off
			        [33, [-0.0_513, 0.0_289, 1.3_799, 0.2_166, -0.2_573, -0.0_871, 0.5_103, -0.0_999]],
			        [47, [-0.4_128, -0.1_320, -0.3_704, 0.1_965, -0.4_116, -0.2_332, -0.3_340, 0.2_247]],
			        # fmt: on
			    ]						)
			@require_torch_gpu
			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							List[str]:
					snake_case__	     =self.get_sd_vae_model(fpaa=_UpperCAmelCase						)
					snake_case__	     =self.get_sd_image(_UpperCAmelCase					,   fpaa=_UpperCAmelCase						)
					snake_case__	     =self.get_generator(_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase					,   generator=_UpperCAmelCase					,   sample_posterior=_UpperCAmelCase						).sample

					assert sample.shape == image.shape

					snake_case__	     =sample[-1, -2:, :2, -2:].flatten().float().cpu()
					snake_case__	     =torch.tensor(_UpperCAmelCase						)

					assert torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   atol=1E-2						)






			@parameterized.expand(
			    [
			        # fmt: off
			        [33, [-0.1_609, 0.9_866, -0.0_487, -0.0_777, -0.2_716, 0.8_368, -0.2_055, -0.0_814], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
			        [47, [-0.2_377, 0.1_147, 0.1_333, -0.4_841, -0.2_506, -0.0_805, -0.0_491, -0.4_085], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
			        # fmt: on
			    ]						)
			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							int:
					snake_case__	     =self.get_sd_vae_model()
					snake_case__	     =self.get_sd_image(_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model(_UpperCAmelCase						).sample

					assert sample.shape == image.shape

					snake_case__	     =sample[-1, -2:, -2:, :2].flatten().float().cpu()
					snake_case__	     =torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice						)

					assert torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   atol=3E-3						)






			@parameterized.expand(
			    [
			        # fmt: off
			        [13, [-0.2_051, -0.1_803, -0.2_311, -0.2_114, -0.3_292, -0.3_574, -0.2_953, -0.3_323]],
			        [37, [-0.2_632, -0.2_625, -0.2_199, -0.2_741, -0.4_539, -0.4_990, -0.3_720, -0.4_925]],
			        # fmt: on
			    ]						)
			@require_torch_gpu
			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Any:
					snake_case__	     =self.get_sd_vae_model()
					snake_case__	     =self.get_sd_image(_UpperCAmelCase					,   shape=(3, 4, 64, 64)						)

					with torch.no_grad():
							snake_case__	     =model.decode(_UpperCAmelCase						).sample

					assert list(sample.shape						) == [3, 3, 512, 512]

					snake_case__	     =sample[-1, -2:, :2, -2:].flatten().cpu()
					snake_case__	     =torch.tensor(_UpperCAmelCase						)

					assert torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   atol=1E-3						)






			@parameterized.expand(
			    [
			        # fmt: off
			        [27, [-0.0_369, 0.0_207, -0.0_776, -0.0_682, -0.1_747, -0.1_930, -0.1_465, -0.2_039]],
			        [16, [-0.1_628, -0.2_134, -0.2_747, -0.2_642, -0.3_774, -0.4_404, -0.3_687, -0.4_277]],
			        # fmt: on
			    ]						)
			@require_torch_gpu
			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Any:
					snake_case__	     =self.get_sd_vae_model(fpaa=_UpperCAmelCase						)
					snake_case__	     =self.get_sd_image(_UpperCAmelCase					,   shape=(3, 4, 64, 64)					,   fpaa=_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model.decode(_UpperCAmelCase						).sample

					assert list(sample.shape						) == [3, 3, 512, 512]

					snake_case__	     =sample[-1, -2:, :2, -2:].flatten().float().cpu()
					snake_case__	     =torch.tensor(_UpperCAmelCase						)

					assert torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   atol=5E-3						)






			@parameterized.expand([(13,), (16,), (27,)]						)
			@require_torch_gpu
			@unittest.skipIf(not is_xformers_available()					,   reason='xformers is not required when using PyTorch 2.0.'						)
			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							List[Any]:
					snake_case__	     =self.get_sd_vae_model(fpaa=_UpperCAmelCase						)
					snake_case__	     =self.get_sd_image(_UpperCAmelCase					,   shape=(3, 4, 64, 64)					,   fpaa=_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model.decode(_UpperCAmelCase						).sample

					model.enable_xformers_memory_efficient_attention()
					with torch.no_grad():
							snake_case__	     =model.decode(_UpperCAmelCase						).sample

					assert list(sample.shape						) == [3, 3, 512, 512]

					assert torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   atol=1E-1						)






			@parameterized.expand([(13,), (16,), (37,)]						)
			@require_torch_gpu
			@unittest.skipIf(not is_xformers_available()					,   reason='xformers is not required when using PyTorch 2.0.'						)
			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Optional[Any]:
					snake_case__	     =self.get_sd_vae_model()
					snake_case__	     =self.get_sd_image(_UpperCAmelCase					,   shape=(3, 4, 64, 64)						)

					with torch.no_grad():
							snake_case__	     =model.decode(_UpperCAmelCase						).sample

					model.enable_xformers_memory_efficient_attention()
					with torch.no_grad():
							snake_case__	     =model.decode(_UpperCAmelCase						).sample

					assert list(sample.shape						) == [3, 3, 512, 512]

					assert torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   atol=1E-2						)






			@parameterized.expand(
			    [
			        # fmt: off
			        [33, [-0.3_001, 0.0_918, -2.6_984, -3.9_720, -3.2_099, -5.0_353, 1.7_338, -0.2_065, 3.4_267]],
			        [47, [-1.5_030, -4.3_871, -6.0_355, -9.1_157, -1.6_661, -2.7_853, 2.1_607, -5.0_823, 2.5_633]],
			        # fmt: on
			    ]						)
			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Optional[int]:
					snake_case__	     =self.get_sd_vae_model()
					snake_case__	     =self.get_sd_image(_UpperCAmelCase						)
					snake_case__	     =self.get_generator(_UpperCAmelCase						)

					with torch.no_grad():
							snake_case__	     =model.encode(_UpperCAmelCase						).latent_dist
							snake_case__	     =dist.sample(generator=_UpperCAmelCase						)

					assert list(sample.shape						) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

					snake_case__	     =sample[0, -1, -3:, -3:].flatten().cpu()
					snake_case__	     =torch.tensor(_UpperCAmelCase						)

					snake_case__	     =3E-3 if torch_device != 'mps' else 1E-2
					assert torch_all_close(_UpperCAmelCase					,   _UpperCAmelCase					,   atol=_UpperCAmelCase						)









'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 				a  (     UpperCamelCase_    :    Any			)       ->	List[str]:
		snake_case__	     =os.path.join(args.tf_model_dir      ,					'parameters.json'			)
		snake_case__	     =json.loads(open(UpperCamelCase_			).read()			)
		if not params:
				raise ValueError(
				    f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""			)
		if not args.output.endswith('.pt'			):
				snake_case__	     =args.output + '.pt'
		snake_case__	     =OrderedDict()
		with tf.device('/CPU:0'			):
				snake_case__	     =tf.train.load_checkpoint(args.tf_model_dir			)
				snake_case__	     =reader.get_variable_to_shape_map()
				for key_name in shapes.keys():
						snake_case__	     =reader.get_tensor(UpperCamelCase_			).astype(np.floataa			)
						if key_name.endswith('/adam_m'			) or key_name.endswith('/adam_v'			):
								continue
						if key_name.startswith('pasts/'			):
								if key_name.startswith('pasts/mlp'			):
										snake_case__	     =int(key_name[9]			)
								elif key_name.startswith('pasts/out'			):
										snake_case__	     =8
								snake_case__	     ='model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
								snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/moe'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/switch_gating/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/softmlp/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/wo/kernel'			) or key_name.endswith('/wi/kernel'			):
										snake_case__	     =key_name[-9:-7]
										for i in range(16			):
												snake_case__	     ='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
												snake_case__	     =(
												    vnp[i].transpose([1, 0]			).copy()
												)  # In Mesh-Tensorflow, it is one array, so it is divided
												snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/mlp'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/p1/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wi.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p1/bias'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wi.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p2/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wo.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p2/bias'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wo.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/ln'			):
								snake_case__	     =int(key_name[8:].split('/'			)[0]			)
								if key_name.endswith('/b'			):
										snake_case__	     ='model.blocks.%d.feed_forward.norm.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/g'			):
										snake_case__	     ='model.blocks.%d.feed_forward.norm.weight' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/att'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/qkv/kernel'			):
										snake_case__	     =vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
										snake_case__	     =state[:, 0, :, :]
										snake_case__	     =state[:, 1, :, :]
										snake_case__	     =state[:, 2, :, :]
										snake_case__	     =(
										    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =(
										    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =(
										    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/o/kernel'			):
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
										snake_case__	     =(
										    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]			).transpose([1, 0]			).copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/an'			):
								snake_case__	     =int(key_name[8:].split('/'			)[0]			)
								if key_name.endswith('/b'			):
										snake_case__	     ='model.blocks.%d.self_attn.norm.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/g'			):
										snake_case__	     ='model.blocks.%d.self_attn.norm.weight' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif (
						    key_name.startswith('model/wte'			)
						    or key_name.startswith('model/wpe'			)
						    or key_name.startswith('model/ete'			)
						):
								snake_case__	     ={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
								    key_name[-3:]
								]
								snake_case__	     ='model.%s.weight' % nlayer
								snake_case__	     =vnp.copy()  # same in embedded
								snake_case__	     =torch.tensor(UpperCamelCase_			)
								if key_name.startswith('model/wte'			):
										snake_case__	     ='lm_head.weight'
										snake_case__	     =vnp.copy()  # same in embedded
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/wob'			):
								snake_case__	     ='final_logits_bias'
								snake_case__	     =vnp.copy()  # same in embedded
								snake_case__	     =state.reshape((1, -1)			)
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name == "model/dense/kernel":
								snake_case__	     ='model.last_project.weight'
								snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name == "model/dense_1/bias":
								snake_case__	     ='model.last_project.bias'
								snake_case__	     =vnp.copy()  # same because it is one dimensional
								snake_case__	     =torch.tensor(UpperCamelCase_			)
		torch.save(UpperCamelCase_      ,					args.output			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						int	=  argparse.ArgumentParser(
			    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
			)
			parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
			parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
			SCREAMING_SNAKE_CASE__     :						str	=  parser.parse_args()
			convert_tf_gptsan_to_pt(args)




